abstract = "A number of researchers who apply genetic programming
(GP) to the analysis of financial data have had success
in using predictability pretests to determine whether
the time series under analysis by a GP contains
patterns that are actually inherently predictable.
However, most studies to date apply no such pretests,
or pretests of any kind. Most previous work in this
area has attempted to use filters to ensure inherent
predictability of the data within a window of a time
series, whereas other works have used multiple time
frame windows under analysis by the GP to provide one
overall GP recommendation. This work, for the first
time, analyses the use of external information about
the price trend of a stock's market sector. This
information is used in a filter to bolster confidence
of a GP-based alert regarding formation of a trend for
the chosen stock. Our results indicate a significant
improvement in trend identification for the majority of
stocks analysed using intraday data.",

notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",